3 papers
cs.RO2026
AURA: Asymptotically Optimal Uncertainty-Robust Replanning Algorithm for Kinodynamic Systems
Seyedali Golestaneh, Zhuoyun Zhong, Donghyung Lee +1
Sampling-based motion planners offer a practical and scalable approach to kinodynamic motion planning, notably for high-dimensional, underactuated, or non-holonomic systems. Howeve…
cs.RO2026
Terminal Matters: Kinodynamic Planning with a Terminal Cost and Learned Uncertainty in Belief State-Cost Space
Zhuoyun Zhong, Seyedali Golestaneh, Constantinos Chamzas
In many real-world robotic tasks, robots must generate dynamically feasible motions that reliably reach desired goals even under uncertainty. Yet existing sampling-based kinodynami…
cs.RO2025
ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation
Zhuoyun Zhong, Seyedali Golestaneh, Constantinos Chamzas
Planning with learned dynamics models offers a promising approach toward versatile real-world manipulation, particularly in nonprehensile settings such as pushing or rolling, where…